Python News: What's New From April 2023 :
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Spring is in bloom, bringing new and exciting developments in the Python world. The last preview release of Python 3.12 before the feature freeze, a new major version of pandas, pip
and PyPI improvements, and PyCon US 2023 are a few of them.
Grab a cup of your favorite beverage, sit back comfortably in your chair, and enjoy a fresh dose of Python news from the past month!
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Python 3.12.0 Alpha 7 Is Now Available
Python 3.12.0 alpha 7 became available to the public on April 4, marking the final alpha version before the planned transition to the beta phase, which will begin a partial feature freeze. Beyond this point, most development efforts will focus on fixing bugs and making small improvements without introducing significant changes in the codebase. But existing features could be changed or dropped until the release candidate phase.
While we’re still a few months away from the final release in October, we already have a pretty good idea about the most notable features that should make it into Python 3.12:
- Ever better error messages
- Support for the Linux
perf
profiler - A step toward multithreaded parallelism
- An
@override
decorator for static typing - More precise
**kwargs
typing - New syntax for specifying generic types
- Parentheses in
assert
statements - Various performance and memory optimizations
- Numerous deprecations and removals
But you don’t have to wait until the fall to get your hands on these upcoming features. You can check them out today by installing a pre-release version of Python, remembering that alpha and beta releases are solely meant for testing and experimenting. So, never use them in production!
If you happen to find something that isn’t working as expected, then don’t hesitate to submit a bug report through Python’s issue tracker on GitHub. Testing pre-release versions of Python is one of the reasons why they’re available to early adopters in the first place. The whole Python community will surely appreciate your help in making the language as stable and reliable as possible.
Note: For a full list of features implemented in the Python 3.12.0 alpha 7 release, have a glimpse at its changelog, which includes links to the respective GitHub tickets.
The Python 3.12.0 alpha 7 release brings us one step closer to the final version, but there’s still a lot of work to be done. These ongoing efforts may sometimes affect the official release schedule, so keep an eye on it and stay tuned for more updates in the coming months.
pandas 2.0 Receives a Major Update With PyArrow Integration
The popular Python data analysis and manipulation library pandas has recently released its latest version, pandas 2.0.0, followed by a patch release shortly after. These updates finalize a release candidate that became available a few months ago.
Historically, pandas has relied on NumPy as its back end for storing DataFrame
and Series
containers in memory. This release introduces an exciting new development in the form of optional PyArrow engine support, providing the Apache Arrow columnar data representation. However, nothing is changing by default, as the developers behind pandas aim to accommodate their large user base and avoid introducing breaking changes.
You now have the option to request the PyArrow back end instead of NumPy, as you can see in the following code snippets:
>>> import pandas as pd
>>> pd.Series([1, 2, None, 4], dtype="int64[pyarrow]")
0 1
1 2
2 <NA>
3 4
dtype: int64[pyarrow]
>>> df = pd.read_csv("file.csv", engine="pyarrow", dtype_backend="pyarrow")
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 21 entries, 0 to 20
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 date 21 non-null date32[day][pyarrow]
1 transaction_no 21 non-null int64[pyarrow]
2 payment_method 21 non-null string[pyarrow]
3 category 21 non-null string[pyarrow]
4 item 21 non-null string[pyarrow]
5 qty 21 non-null double[pyarrow]
6 price 21 non-null string[pyarrow]
7 subtotal 21 non-null string[pyarrow]
8 comment 21 non-null string[pyarrow]
dtypes: date32[day][pyarrow](1), double[pyarrow](1), int64[pyarrow](1), string[pyarrow](6)
memory usage: 1.8 KB
Read the full article at https://realpython.com/python-news-april-2023/ »
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